A Deep Learning Approach to Respiratory Disease Classification Using Lung Sound Visualization for Telemedicine Applications - Wahyudi, Andi Enal; Batau, Radus; Aziz, Firman; Jeffry, Jeffry (Universitas Pancasakti, 2025)
This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.